Model Comparison
DeepSeek-V2.5 vs MiniMax M1 80KWhich is better in 2026?
MiniMax M1 80K significantly outperforms across most benchmarks. DeepSeek-V2.5 is 5.5x cheaper per token.
Verdict: DeepSeek-V2.5 vs MiniMax M1 80K — which is better?
DeepSeek-V2.5 (by DeepSeek) and MiniMax M1 80K (by MiniMax) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V2.5 outperforms in 0 benchmarks, while MiniMax M1 80K is better at 1 benchmark (SWE-Bench Verified). MiniMax M1 80K significantly outperforms across most benchmarks.
On price, DeepSeek-V2.5 is roughly 5.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiniMax M1 80K also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V2.5 if…
- cost matters — it's about 5.5x cheaper per token
Choose MiniMax M1 80K if…
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Jun 2025
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V2.5 outperforms in 0 benchmarks, while MiniMax M1 80K is better at 1 benchmark (SWE-Bench Verified).
MiniMax M1 80K significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 3.9x cheaper than MiniMax M1 80K ($0.55/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 7.9x cheaper than MiniMax M1 80K ($2.20/1M tokens).
In conclusion, MiniMax M1 80K is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiniMax M1 80K has 220.0B more parameters than DeepSeek-V2.5, making it 93.2% larger.
Context Window
Maximum input and output token capacity
MiniMax M1 80K accepts 1,000,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. MiniMax M1 80K can generate longer responses up to 40,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while MiniMax M1 80K uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while MiniMax M1 80K was released on 2025-06-16.
MiniMax M1 80K is 13 months newer than DeepSeek-V2.5.
May 8, 2024
2.1 years ago
Jun 16, 2025
1.0 years ago
1.1yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. MiniMax M1 80K is available from Novita.
DeepSeek-V2.5
MiniMax M1 80K
Outputs Comparison
Key Takeaways
DeepSeek-V2.5
View detailsDeepSeek
MiniMax M1 80K
View detailsMiniMax
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and MiniMax M1 80K side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V2.5 vs MiniMax M1 80K.